added project page for taxabind

index.html

@@ -114,6 +114,7 @@ <h2>Publications</h2>
                 <a href="https://arxiv.org/pdf/2411.00683">arxiv</a> /
                 <a href="data/taxabind.bib">bibtex</a> /
                 <a href="https://github.com/mvrl/TaxaBind">code</a>
+                <a href="https://vishu26.github.io/taxabind/index.html">project page</a>
                 <p></p>
                 <p>TaxaBind is a suite of multimodal models useful for downstream ecological tasks covering six modalities: ground-level image, geographic location, satellite image, text, audio, and environmental features.</p>
               </td>
@@ -197,43 +198,44 @@ <h2>Publications</h2>
             </td>
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           <tr>
             <td width="100%" valign="middle">
-              <a href="https://arxiv.org/pdf/2407.09672" id="MCG_journal">
-                <span class="papertitle">Mixed-View Panorama Synthesis using Geospatially Guided Diffusion.</span>
+              <a href="https://arxiv.org/pdf/2312.08334.pdf" id="MCG_journal">
+                <span class="papertitle">LD-SDM: Language-Driven Hierarchical Species Distribution Modeling</span>
               </a>
               <br>
-              Xiong Zhexiao, Xing Xin, Workman Scott, <strong>Khanal Subash</strong>, Jacobs Nathan
+              Sastry Srikumar, Xin Xing, Dhakal Aayush, <strong>Khanal Subash</strong>, Ahmad Adeel, and Jacobs Nathan
               <br>
               <em>preprint</em>, 2024
               <br>
-              <a href="https://arxiv.org/pdf/2407.09672">arxiv</a> /
-              <a href="data/mixed.bib">bibtex</a> 
+              <a href="https://arxiv.org/pdf/2312.08334.pdf">arxiv</a> /
+              <a href="data/ldsdm.bib">bibtex</a> 
               <p></p>
               <p>
-              This work introduces the task of mixed-view panorama synthesis, where the goal is to synthesize a novel panorama given a small set of input panoramas and a satellite image of the area.</p>
+               We introduced a novel approach for species distribution modeling that uses a large-language model to generate a representation of species. This provides flexibility to generate range maps at different levels of the taxonomic hierarchy and for unseen species.</p>
             </td>
           </tr>
-
+          
           <tr>
             <td width="100%" valign="middle">
-              <a href="https://arxiv.org/pdf/2312.08334.pdf" id="MCG_journal">
-                <span class="papertitle">LD-SDM: Language-Driven Hierarchical Species Distribution Modeling</span>
+              <a href="https://arxiv.org/pdf/2407.09672" id="MCG_journal">
+                <span class="papertitle">Mixed-View Panorama Synthesis using Geospatially Guided Diffusion.</span>
               </a>
               <br>
-              Sastry Srikumar, Xin Xing, Dhakal Aayush, <strong>Khanal Subash</strong>, Ahmad Adeel, and Jacobs Nathan
+              Xiong Zhexiao, Xing Xin, Workman Scott, <strong>Khanal Subash</strong>, Jacobs Nathan
               <br>
               <em>preprint</em>, 2024
               <br>
-              <a href="https://arxiv.org/pdf/2312.08334.pdf">arxiv</a> /
-              <a href="data/ldsdm.bib">bibtex</a> 
+              <a href="https://arxiv.org/pdf/2407.09672">arxiv</a> /
+              <a href="data/mixed.bib">bibtex</a> 
               <p></p>
               <p>
-               We introduced a novel approach for species distribution modeling that uses a large-language model to generate a representation of species. This provides flexibility to generate range maps at different levels of the taxonomic hierarchy and for unseen species.</p>
+              This work introduces the task of mixed-view panorama synthesis, where the goal is to synthesize a novel panorama given a small set of input panoramas and a satellite image of the area.</p>
             </td>
           </tr>
 
+
+
             <tr>
               <td width="100%" valign="middle">
                 <a href="https://arxiv.org/pdf/2206.14841.pdf" id="MCG_journal">